PDM: Time-series forecasting models with 6M–119M parameters Flashmind, a UAE-based company, released PDM, a family of time-series forecasting models named Nano, Pico and Femto with 6M–119M parameters, available free and through the Flashmind API. PDM-Nano ranks #1 among zero-shot foundation models by normalized MASE on both the GIFT-Eval and TIME leaderboards, and in a synthetic café example adding covariates such as discounts, opening hours and past temperatures cut mean absolute error from 29.50 to 18.00 drinks over a 14-day forecast using 512 days of history. All three models predict 21 quantiles, and the API lets users choose which to return. Today we’re introducing PDM , a family of AI models that predict future values from past data. Built by Flashmind in the UAE, Nano, Pico and Femto can forecast demand, energy use, sensor readings and other measurements that change over time. All three are available to try for free and use through the Flashmind API https://www.flashmind.com/en/docs/api . PDM works on new data without requiring you to train a model of your own. This is called zero-shot forecasting . At launch, PDM-Nano ranks 1 among zero-shot foundation models by normalized MASE , a measure of forecast error, on both GIFT-Eval https://huggingface.co/spaces/Salesforce/GIFT-Eval and TIME https://huggingface.co/spaces/Real-TSF/TIME-leaderboard . 1 https://www.flashmind.com/en/research/introducing-pdm ref-1 , 2 https://www.flashmind.com/en/research/introducing-pdm ref-2 From past data to a forecast Many decisions depend on knowing what is likely to happen next. A shop orders stock before customers arrive. An energy operator plans supply before tomorrow’s demand is known. Forecasts help people make those decisions using the measurements they already have. A time series is simply a sequence of measurements over time: daily sales, hourly electricity use or readings from a machine. You give PDM that history and choose how far ahead to predict, known as the forecast horizon . For daily sales, a 14-step forecast predicts the next 14 days. PDM is a forecasting foundation model . It learns patterns across many time series during training, then applies what it has learned to a new series. That is what lets you move directly from supplying data to receiving a forecast, without a separate training process for each shop, building or machine. Forecasting with context Past measurements are only part of the picture. A planned discount, a change in opening hours or a price increase can affect what happens next. PDM can use that additional information alongside the series you want to predict. These extra inputs are called covariates . Consider a café deciding how much stock to prepare for the next two weeks. It plans a 20% discount and will be closed for one day. The chart below compares two PDM-Nano forecasts of its daily iced-coffee orders: one uses sales history alone; the other also uses past temperatures, discounts and opening hours, including the planned promotion and closure. With the extra information, the forecast anticipates the promotion surge and the drop on the closed day. In this example, the average size of the daily prediction error falls from 29.50 to 18.00 drinks . This measure is called mean absolute error , or MAE. The shaded band shows uncertainty around the prediction. Its middle line is the median , the model’s central estimate; the band spans its 10th to 90th percentiles. These levels are called quantiles . All three models predict 21 quantiles, and the API lets you choose which to return. They help you plan for a range of outcomes, such as holding more stock when running out would be costly. How we set up the café example Both forecasts use the same 512 days of sales history and predict the next 14 days . The forecast with covariates receives temperature only for past days. Discounts and opening hours include both past values and the café’s known plans for the next 14 days. Future sales are withheld from both forecasts and used only to measure their errors. Orders are constrained to nonnegative integers. The shaded band belongs to the forecast with covariates. These results come from this synthetic example, rather than a benchmark average. The expandable reproduction script at the end of the article runs both forecasts in one API request. Three sizes, the same capabilities Nano, Pico and Femto share a custom architecture designed for time-series forecasting. They differ in capacity, measured in parameters : the values a model learns during training. Nano is the largest; Femto is the smallest. Our flagshipPDM Nano https://www.flashmind.com/en/models/pdm pdm-nano 119M parameters The middle modelPDM Pico https://www.flashmind.com/en/models/pdm pdm-pico 32.2M parameters The smallest PDMPDM Femto https://www.flashmind.com/en/models/pdm pdm-femto 6.18M parameters Accuracy improves with model size on every aggregate metric we report across GIFT-Eval, TIME and fev-bench. The capabilities stay the same across all three sizes. Alongside forecasting a single series, each model can forecast several related series together, such as sales across stores or readings from connected sensors. This is multivariate forecasting . The playground’s linked-sensor example lets you compare it with forecasting a sensor in isolation. All three also support past and known-future covariates, missing observations, up to 16,336 past observations per series as context and a 960-step native forecast horizon . A step follows the interval of your data: one hour for an hourly series, one day for a daily series. You can switch models by changing the model name in the same API call. How PDM compares Benchmarks test models on collections of forecasting problems so their accuracy can be compared. Two key measures are MASE mean absolute scaled error , which measures the accuracy of the central prediction relative to a simple baseline, and CRPS continuous ranked probability score , which evaluates possible outcomes and their predicted probabilities. Lower scores are better for both. On fev-bench, results are shown as skill scores, where higher is better. As of November 1, 2026, PDM-Nano ranks 1 among zero-shot foundation models by normalized MASE on both GIFT-Eval https://huggingface.co/spaces/Salesforce/GIFT-Eval and TIME https://huggingface.co/spaces/Real-TSF/TIME-leaderboard , with scores of 0.637 and 0.625 , respectively. Both benchmarks’ maintainers independently reproduced our results for all three models and merged our submissions, including reproduction code. 6 https://www.flashmind.com/en/research/introducing-pdm ref-6 , 7 https://www.flashmind.com/en/research/introducing-pdm ref-7 Our evaluations cover 97 GIFT-Eval configurations, 98 TIME configurations and 100 fev-bench tasks .